Visualization, modeling and validation of chromatin interaction data

染色质相互作用数据的可视化、建模和验证

• 批准号: 10318167
• 负责人: Feng Yue
• 金额: $ 39.5万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10318167
• 关键词: 3-Dimensional; Address; CRISPR/Cas technology; Cell Line; Cell physiology; Cells; ChIP-seq; Chromatin; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Chromatin Remodeling Factor; Chromosome Territory; Communities; Complex; Computer Models; Computing Methodologies; Country; Coupled; Data; Data Analyses; Distal; Elements; Environment; Event; Frequencies; Gene Expression; Gene Expression Regulation; Genes; Genome; Genome engineering; Genomic Segment; Genomics; Hi-C; Intuition; Knock-out; Learning; Link; Machine Learning; Measures; Mediating; Methods; Modeling; Molecular; Procedures; Regulator Genes; Regulatory Element; Research; Resolution; Statistical Models; Structure; System; Techniques; Technology; Tissues; Validation; Visit; Visualization; base; cell type; chromosome conformation capture; convolutional neural network; cost; epigenome; epigenomics; experimental study; genome annotation; genome browser; genome-wide; genomic locus; high throughput technology; histone modification; human embryonic stem cell; interest; mammalian genome; performance tests; predictive modeling; prototype; random forest; repository; transcription factor; transcriptome sequencing; web site

The three dimensional (3D) organization of mammalian genomes is tightly linked to gene regulation, as it can reveal the physical interactions between distal regulatory elements and their target genes. Several recent high- throughput technologies based on Chromatin Conformation Capture (3C) have emerged (such as 4C, 5C, Hi-C and ChIA-PET) and given us an unprecedented opportunity to study the higher-order genome organization. Among them, Hi-C technology is of particular interest due to its unbiased genome-wide coverage that can measure chromatin interaction intensities between any two given genomic loci. However, Hi-C data analysis and interpretation are still in the early stages. One of the main challenges is how to efficiently visualize chromatin interaction data, so that the scientific community to visualize and use it for their own research. In addition, due to the complex experimental procedure and high sequencing cost, Hi-C has only been performed in a limited number of cell/tissue types. Finally, the underlying mechanism of chromatin interactions remains largely unclear. Therefore, the PI will propose the following aims: Aim 1. Build an interactive and customizable 3D genome browser. We will build an interactive and customizable 3D browser, which allows users to navigate Hi-C data and other high-throughput chromatin organization data, including ChIA-PET and Capture Hi-C. We have built a prototype of the 3D genome browser (www.3dgenome.org). Our browser will allow users to conveniently browse chromatin interaction data with other data types (such as ChIP-Seq and RNA-Seq) from the genomic region in the same window simultaneously. Our system will also empower the users to create their own session and query their own Hi-C and other epigenomic data. Aim 2. Impute chromatin interaction using other genomic/epigenomic information. We will predict Hi-C interaction frequencies using other available genomic and epigenomic data in the same cell type, such as ChIP-Seq data for histone modifications and transcription factors. We will build our prediction model and then systematically impute Hi-C interaction matrices for all 127 cell types whose epigenomes are available thanks to recent effort by the ENCODE and Roadmap Epigenome projects. Aim 3. Perform validation experiments for computational method in aim 1 and 2. We will perform 20 3C experiments in hESC and GM cell lines, coupled with genome engineering by CRISPR/Cas9, to evaluate Hi-C prediction method in aim 2.

哺乳动物基因组的三维（3D）组织与基因调控密切相关，因为它可以 揭示了远端调控元件与其靶基因之间的物理相互作用。最近几次高- 已经出现了基于染色质构象捕获（3C）的通量技术（例如4C、5C、Hi-C 和ChIA-PET），并给了我们一个前所未有的机会来研究更高层次的基因组组织。 其中，Hi-C技术由于其无偏的全基因组覆盖而特别令人感兴趣， 测量任何两个给定基因组位点之间的染色质相互作用强度。 然而，Hi-C数据分析和解释仍处于早期阶段。其中一个主要挑战是， 有效地可视化染色质相互作用数据，以便科学界可视化并将其用于 自己的研究。此外，由于实验程序复杂和测序成本高，Hi-C 仅在有限数量的细胞/组织类型中进行。最后， 染色质的相互作用仍然很不清楚。因此，PI将提出以下目标： 目标1。构建一个交互式和可定制的3D基因组浏览器。我们将建立一个互动和 可定制的3D浏览器，允许用户浏览Hi-C数据和其他高通量染色质 组织数据，包括ChIA-PET和Capture Hi-C。我们已经建立了一个3D基因组浏览器的原型 （www.example.com）中找到。我们的浏览器将允许用户方便地浏览染色质相互作用数据， 来自同一窗口中基因组区域的其他数据类型（如ChIP-Seq和RNA-Seq） 同步我们的系统还将使用户能够创建自己的会话和查询自己的Hi-C 和其他表观基因组数据。目标2.使用其他基因组/表观基因组的插补染色质相互作用 信息.我们将使用其他可用的基因组和表观基因组数据预测Hi-C相互作用频率 在相同的细胞类型，如组蛋白修饰和转录因子的ChIP-Seq数据。我们将建立 我们的预测模型，然后系统地估算所有127种细胞类型的Hi-C相互作用矩阵， 由于ENCODE和Roadmap表观基因组计划最近的努力，表观基因组是可用的。目标3。 对目标1和2中的计算方法进行验证实验。我们将执行20 3C 在hESC和GM细胞系中进行实验，结合CRISPR/Cas9基因组工程，以评估Hi-C 目标2中的预测方法。